From Latent Representations to Causal Health States: Degradation-Aware Geometry for Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction is commonly formulated as direct regression from condition-monitoring signals to a scalar lifetime estimate. While increasingly powerful models achieve strong predictive performance, this formulation does not explicitly ensure that their learned representations reflect the underlying degradation process. We introduce Health-Aware Manifold Learning (HAML), a new representation-backend-portable framework that constructs an explicit degradation geometry over latent representations before RUL prediction. HAML first encodes causal sensor windows into a low-dimensional representation without RUL or lifecycle-progress supervision. Using training data only, lifecycle progress guides the construction of a sparse degradation-aware landmark graph that penalises cross-stage shortcuts through a progress-aware graph metric. Distributed healthy and failure regions define a graph-geodesic health coordinate, which a persistent level-velocity filter transforms into a bounded, non-decreasing Health Index (HI) using only observations available up to the current cycle. The resulting health dynamics and complementary manifold geometry support downstream RUL estimation. Experiments on all four C-MAPSS aircraft-engine subsets and the PHM12 bearing dataset demonstrate competitive RUL prediction, meaningful lifecycle alignment, and favourable late-life error behaviour, while controlled C-MAPSS sensor perturbations demonstrate robustness to test-time noise. Controlled ablations clarify the roles of individual components, while alternative representation backends demonstrate HAML's portability beyond UMAP. These results show that explicitly modelling degradation geometry and its temporal evolution offers a principled alternative to unconstrained sensor-to-RUL regression.